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The need to monitor industrial processes, detecting changes in process parameters in order to promptly correct problems that may arise, generates a particular area of interest. This is particularly critical and complex when the measured…

Applications · Statistics 2019-05-07 Javier Neira Rueda , Andres Carrion Garcia

Different ways of extracting parameters of interest from combined data sets of separate experiments are investigated accounting for the systematic errors. It is shown, that the frequentist approach may yield larger $\chi^2$ values when…

High Energy Physics - Experiment · Physics 2018-04-17 R. Orava , O. V. Selyugin

We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary, or locally stationary, time…

Statistics Theory · Mathematics 2013-02-04 Zhibiao Zhao , Xiaoye Li

Although the study of weak convergence of superpositions of point processes to the Poisson process dates back to the work of Grigelionis in 1963, it was only recently that Schuhmacher [Stochastic Process. Appl. 115 (2005) 1819--1837]…

Probability · Mathematics 2011-05-10 Louis H. Y. Chen , Aihua Xia

The paper proposes a formal estimation procedure for parameters of the fractional Poisson process (fPp). Such procedures are needed to make the fPp model usable in applied situations. The basic idea of fPp, motivated by experimental data…

Methodology · Statistics 2018-06-08 Dexter Cahoy , Vladimir V. Uchaikin , Wojbor A. Woyczynski

We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical…

Machine Learning · Computer Science 2012-07-02 Yi-Hao Kao , Benjamin Van Roy

We correct some tables and figures in [A.P. Bustamante and R.C. Calleja, Physica D: Nonlinear Phenomena, 395 (2019), pp. 15-23, arXiv:1712.05476]. We also report on the new computations that verify the accuracy of the data and extend the…

Dynamical Systems · Mathematics 2021-02-24 Adrian P. Bustamante , Renato C. Calleja

In this note, it is shown that the results claimed in the paper [1]---as well as the examples presented there---are, unfortunately, incorrect.

Dynamical Systems · Mathematics 2019-09-12 Alejandro Donaire , Jose Guadalupe Romero , Romeo Ortega

A case is made that researchers are interested in studying processes. Often the inferences they are interested in making are about the process and its associated population. On other occasions, a researcher may be interested in making an…

Other Statistics · Statistics 2022-06-20 Charles W. Champ , Andrew V. Sills

We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter. The estimation is to be done online, i.e. the…

Optimization and Control · Mathematics 2018-10-16 Simone Carlo Surace , Jean-Pascal Pfister

Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress. Although the usefulness of such tools has been widely recognized in real world…

Machine Learning · Computer Science 2019-11-20 Shengcai Liu , Ke Tang , Yunwen Lei , Xin Yao

Misprints in eq. (7), which propagate up to eq. (13), are corrected. References are updated.

Condensed Matter · Physics 2008-02-03 Yong-Shi Wu

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference…

Artificial Intelligence · Computer Science 2022-12-23 Myriam Bounhas , Henri Prade , Gilles Richard

Stochastic processes find applications in modelling systems in a variety of disciplines. A large number of stochastic models considered are Markovian in nature. It is often observed that higher order Markov processes can model the data…

Probability · Mathematics 2021-04-13 Suryadeepto Nag

Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in…

Methodology · Statistics 2018-08-14 Alex Reinhart

In previous work, we proposed a method for leveraging efficient classical simulation algorithms to aid in the analysis of large-scale fault tolerant circuits implemented on hypothetical quantum information processors. Here, we extend those…

Quantum Physics · Physics 2014-02-12 Daniel Puzzuoli , Christopher Granade , Holger Haas , Ben Criger , Easwar Magesan , D. G. Cory

The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that are prohibitively expensive on large datasets. Oftentimes,…

Machine Learning · Computer Science 2023-09-26 Mo Tiwari

This paper proposes an algorithm to estimate the parameters, including time delay, of continuous time systems based on instrumental variable identification methods. To overcome the multiple local minima of the cost function associated with…

Statistics Theory · Mathematics 2018-07-24 Huong Ha , James S. Welsh , Mazen Alamir

Rejoinder to "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong [arXiv:1104.3073]

Methodology · Statistics 2012-01-09 Yingcun Xia , Howell Tong

The subject matter of the article is a model of calculating the user similarity coefficients of the recommendation systems. The goal is the development of the improved model of user similarity coefficients calculation for recommendation…

Information Retrieval · Computer Science 2020-11-11 Yelyzaveta Meleshko , Oleksandr Drieiev , Anas Mahmoud Al-Oraiqat
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